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Found 28 Skills
View Langfuse trace details. Use when checking specific trace input/output, debugging LLM calls, or analyzing costs.
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
View Langfuse session details with all traces. Use when analyzing conversation flows, checking session costs, or debugging multi-turn interactions.
List Langfuse traces with filtering options. Use when checking recent LLM calls, debugging issues, or monitoring costs.
List all Langfuse prompts with their labels and versions. Use when checking available prompts, verifying label assignments, or getting an overview of prompt status.
Set up Langfuse local development workflow with hot reload and debugging. Use when developing LLM applications locally, debugging traces, or setting up a fast iteration loop with Langfuse. Trigger with phrases like "langfuse local dev", "langfuse development", "debug langfuse traces", "langfuse hot reload", "langfuse dev workflow".
Query Langfuse traces for debugging LLM calls, analyzing token usage, and investigating workflow executions. Use when debugging AI/LLM behavior, checking trace data, or analyzing observability metrics.
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
LLM cost tracking with Langfuse for cached responses. Use when monitoring cache effectiveness, tracking cost savings, or attributing costs to agents in multi-agent systems.
Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse LLM tracing, and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
Adds tracing, telemetry, and observability to an assistant-ui backend. Use when wiring an AI SDK route handler (streamText/generateText, toUIMessageStreamResponse) to a tracing backend: Langfuse via OpenTelemetry (LangfuseSpanProcessor and NodeSDK in instrumentation.ts, experimental_telemetry isEnabled, propagateAttributes with traceName/userId/sessionId, langfuseSpanProcessor.forceFlush on serverless), LangSmith via wrapAISDK(ai) from langsmith/experimental/vercel (createLangSmithProviderOptions, awaitPendingTraceBatches), or Helicone via createOpenAI baseURL https://oai.helicone.ai/v1 with the Helicone-Auth header. Also covers rendering collected spans with @assistant-ui/react-o11y headless primitives (SpanResource, SpanPrimitive Root/Indent/CollapseToggle/StatusIndicator/TypeBadge/Name/Children, SpanByIndexProvider, SpanData/SpanState) mounted via useAui/AuiProvider from @assistant-ui/store. Use for missing or empty traces, edge vs nodejs runtime telemetry, serverless flush issues, or trace waterfalls.
Discovers, tests, and manages remote SSH infrastructure hosts and Docker services across 5 hosts (infra.local, deus, homeassistant, pi4-motor, armitage). Use when checking infrastructure status, verifying service connectivity, managing Docker containers, troubleshooting remote services, or before using remote resources (MongoDB, Langfuse, OTLP, Neo4j). Triggers on "check infrastructure", "connect to infra/deus/ha", "test MongoDB on infra", "view Docker services", "verify connectivity", "troubleshoot remote service", "what services are running", or when remote connections fail.